Posts Tagged Dynamic temporary alignment algorithm

[Abstract + References] Upper Limb Rehabilitation with Virtual Environments – Conference paper

Abstract

In this article an application is developed based on 3D environments for the upper limbs rehabilitation, with the aim of performing the measurement of rehabilitation movements that the patient makes. A robotic glove is used for virtualized the movements with the hand. The hand movements are sent to a mathematical processing software which runs an algorithm to determine if the rehabilitation movement is right. Through virtual reality environments, the injured patients see the correct way to perform the movement and also shows the movements that the patient makes with the robotic glove prototype. This system allows to evaluate the protocol of upper limbs rehabilitation, with the continuous use of this system the injured patient can see how his condition evolves after performing several times the proposed virtual tasks.

References

  1. 1.
    Roy, R., Sarkar, M.: Knowledge, firm boundaries, and innovation: mitigating the incumbent’s curse during radical technological change: mitigating incumbent’s curse during radical discontinuity. Strateg. Manag. J. 37, 835–854 (2016)CrossRefGoogle Scholar
  2. 2.
    Van der Loos, H.F.M., Reinkensmeyer, D.J., Guglielmelli, E.: Rehabilitation and Health Care Robotics. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics. SHB, pp. 1685–1728. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-32552-1_64CrossRefGoogle Scholar
  3. 3.
    Meng, W., Liu, Q., Zhou, Z., Ai, Q., Sheng, B., Xie, S.: (Shane): Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics 31, 132–145 (2015)CrossRefGoogle Scholar
  4. 4.
    Vanoglio, F., et al.: Feasibility and efficacy of a robotic device for hand rehabilitation in hemiplegic stroke patients: a randomized pilot controlled study. Clin. Rehabil. 31, 351–360 (2017)CrossRefGoogle Scholar
  5. 5.
    Chiri, A., Vitiello, N., Giovacchini, F., Roccella, S., Vecchi, F., Carrozza, M.C.: Mechatronic design and characterization of the index finger module of a hand exoskeleton for post-stroke rehabilitation. IEEE/ASME Trans. Mechatron. 17, 884–894 (2012)CrossRefGoogle Scholar
  6. 6.
    In, H., Kang, B.B., Sin, M., Cho, K.-J.: Exo-Glove: a wearable robot for the hand with a soft tendon routing system. IEEE Robot. Autom. Mag. 22, 97–105 (2015)CrossRefGoogle Scholar
  7. 7.
    Borboni, A., Mor, M., Faglia, R.: Gloreha—hand robotic rehabilitation: design, mechanical model, and experiments. J. Dyn. Syst. Meas. Control 138, 111003 (2016)CrossRefGoogle Scholar
  8. 8.
    Mourtzis, D., Zogopoulos, V., Vlachou, E.: Augmented reality application to support remote maintenance as a service in the robotics industry. Procedia CIRP 63, 46–51 (2017)CrossRefGoogle Scholar
  9. 9.
    Zorcec, T., Robins, B., Dautenhahn, K.: Getting engaged: assisted play with a humanoid robot Kaspar for children with severe autism. In: Kalajdziski, S., Ackovska, N. (eds.) ICT 2018. CCIS, vol. 940, pp. 198–207. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00825-3_17CrossRefGoogle Scholar
  10. 10.
    Seo, N.J., Arun Kumar, J., Hur, P., Crocher, V., Motawar, B., Lakshminarayanan, K.: Usability evaluation of low-cost virtual reality hand and arm rehabilitation games. J. Rehabil. Res. Dev. 53, 321–334 (2016)CrossRefGoogle Scholar
  11. 11.
    Xiloyannis, M., Cappello, L., Khanh, D.B., Yen, S.-C., Masia, L.: Modelling and design of a synergy-based actuator for a tendon-driven soft robotic glove. In: 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 1213–1219. IEEE, Singapore (2016)Google Scholar
  12. 12.
    Ben-Tzvi, P., Ma, Z.: Sensing and force-feedback exoskeleton (SAFE) robotic glove. IEEE Trans. Neural Syst. Rehabil. Eng. 23, 992–1002 (2015)CrossRefGoogle Scholar
  13. 13.
    Polygerinos, P., Galloway, K.C., Sanan, S., Herman, M., Walsh, C.J.: EMG controlled soft robotic glove for assistance during activities of daily living. In: 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 55–60. IEEE, Singapore (2015)Google Scholar
  14. 14.
    Guindo, J., Martínez-Ruiz, M.D., Gusi, G., Punti, J., Bermúdez, P., Martínez-Rubio, A.: Métodos diagnósticos de la enfermedad arterial periférica. Importancia del índice tobillo-brazo como técnica de criba. Revista Española de Cardiología 09, 11–17 (2009)CrossRefGoogle Scholar
  15. 15.
    Chen, D., Liu, H., Ren, Z.: Application of wearable device HTC VIVE in upper limb rehabilitation training. In: 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 1460–1464. IEEE, Xi’an (2018)Google Scholar
  16. 16.
    Li, Q., Wang, D., Du, Z., Sun, L.: A novel rehabilitation system for upper limbs. In: 2005 27th Annual Conference on IEEE Engineering in Medicine and Biology, pp. 6840–6843. IEEE, Shanghai (2005)Google Scholar
  17. 17.
    Duarte, E., et al.: Rehabilitación del ictus: modelo asistencial. Rehabilitación 44, 60–68 (2010)CrossRefGoogle Scholar
  18. 18.
    Shen, J., Bao, S.-D., Yang, L.-C., Li, Y.: The PLR-DTW method for ECG based biometric identification. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5248–5251. IEEE, Boston (2011)Google Scholar
19.
Piyush Shanker, A., Rajagopalan, A.N.: Off-line signature verification using DTW. Pattern Recogn. Lett. 28, 1407–1414 (2007)CrossRefGoogle Scholar

via Upper Limb Rehabilitation with Virtual Environments | SpringerLink

, , , , , , , ,

Leave a comment

%d bloggers like this: